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Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations
Parallel Gaussian Process Regression Low-Rank Covariance Matrix Approximations
2013/6/14
Gaussian processes (GP) are Bayesian non-parametric models that are widely used for probabilistic regression. Unfortunately, it cannot scale well with large data nor perform real-time predictions due ...
Sharp analysis of low-rank kernel matrix approximations
Sharp analysis low-rank kernel matrix approximations
2012/9/18
We consider supervised learning problems within the positive-definite kernel framework,such as kernel ridge regression, kernel logistic regression or the support vector machine. With kernels leading t...
On Low Rank Matrix Approximations with Applications to Synthesis Problem in Compressed Sensing
Low Rank Matrix Approximations Applications Synthesis Problem Compressed Sensing
2010/3/9
We consider the synthesis problem of Compressed Sensing –given s and an M×n
matrix A, extract from it an m × n submatrix Am, certified to be s-good, with m
as small as possible. Starting from the ve...